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thinformatics
AI INTEGRATIONS

{{Bringing AI straight into your existing systems}}

We integrate AI capabilities straight into your existing applications and processes. Your teams benefit from AI exactly where they already work every day.

ESPECIALLY SUITED FOR
Development
AI Integrations
APIs
Azure OpenAI
Legacy systems
Guardrails
AI Integrations
AI Powered
Entry points
Identified
AI
Embedded
Operation
Dependable
AI
in the process
No system
switching
Securely
Connected
THE SERVICE

AI right inside your applications

We integrate AI capabilities where your teams work every day, so value arises {{right in the flow of work}}, without introducing new tools.

General
Deep dive
1

Application analysis

We identify where AI creates real value in your existing applications.

Use case mapping, process analysis, feasibility, data availability, prioritisation, ROI framing

2

Model connection

We connect suitable language models securely to your systems and interfaces.

Azure OpenAI, Anthropic, REST APIs, SDKs, prompt templates, streaming

3

Knowledge grounding

AI reaches your own knowledge and delivers answers you can back with sources.

RAG, vector databases, embeddings, grounding, chunking, access filters, source citations

4

Process integration

AI capabilities run inside existing processes, with no context switch for users.

Webhooks, event triggers, Microsoft 365, Teams, Power Automate, connectors

5

Security & governance

Access, data and model usage stay controlled and traceable.

Roles, permissions, data classification, prompt filters, audit logs, EU region

6

Operation & monitoring

We watch quality, cost and availability of the AI capabilities in daily operation.

Observability, token metrics, latency monitoring, evaluations, fallback strategies, cost control

OUR APPROACH

AI integration in five steps

1

Analysis

We identify where AI creates real value.

1

Analysis

We analyse applications and processes and assess use cases by feasibility, data availability and benefit. Together we prioritise the cases with the best ratio of impact to effort.

You receive a solid basis for deciding which integration is built first. The prioritised use case goes straight into the technical connection.

Use case mapping
Feasibility rating
Prioritisation list
ROI framing
2

Connection

We connect language models securely to your systems.

2

Connection

We connect suitable language models to your systems and interfaces through REST APIs and SDKs, for instance Azure OpenAI or Anthropic. Prompt templates and streaming settle how the function behaves.

The AI capability is technically available and embedded in your interface. On this basis it gains access to your knowledge in the next step.

Model connection
API interfaces
Prompt templates
Test environment
3

Knowledge grounding

AI reaches your own knowledge with sources you can check.

3

Knowledge grounding

We connect the AI to your approved sources through RAG and use embeddings, vector search and chunking. Access filters and source citations deliver answers that are verifiable and respect permissions.

Answers rest on your own content and stay open to checking. The solid knowledge base is then embedded in your real processes.

RAG pipeline
Vector index
Access filters
Source citations
4

Process integration

AI capabilities run inside processes without breaks.

4

Process integration

We embed the AI capabilities straight into existing processes through webhooks, event triggers and connectors, for instance in Microsoft 365, Teams and Power Automate. The AI works in the context users know.

Teams use the AI exactly where they work every day, with no tool switching. The productive process is then safeguarded and monitored.

Process connectors
Event triggers
In-app functions
Integration test
5

Operation

We secure and watch the AI capabilities continuously.

5

Operation

We govern access, data classification and prompt filters, log through audit trails and run in the EU region. Observability, token metrics and fallback strategies watch quality, cost and availability.

You receive a controlled, maintainable solution that delivers dependable value day to day. From the operational data the integration grows where the benefit is confirmed.

Governance rules
Monitoring dashboard
Audit logs
Operations manual
YOUR BENEFITS

Why {{thinformatics}}

AI where work happens

Teams benefit right inside the applications they know.

No system switching

AI comes into existing processes instead of replacing them.

Securely connected

Models are tied to your data over secure APIs.

The fitting model

We choose the model by task, cost and data protection.

Run compliantly

Guardrails, data protection and the EU AI Act are accounted for.

Measurable benefit

The value becomes visible and the function improves.

Frequently asked {{questions}}

FAQ

Answers to the questions we are asked most often about integrating AI into existing applications.

What does AI integration into existing applications mean in concrete terms?
AI integration brings AI capabilities straight to where your teams already work every day, for instance into existing business applications, portals or workflows. Instead of a separate silo we extend the systems you have deliberately with abilities such as text understanding, search, summarisation or assistance. Value arises without switching to unfamiliar tools and without a break in routines people know. The benefit becomes tangible because AI supports the concrete task in a familiar context.
Which applications and routines suit an AI integration?
Particularly suitable are routines with a high share of text, search, repetition or manual preparation of information. Examples are finding knowledge, summarising content, supporting customer contact or pre-structuring routine tasks. As selection criteria we recommend evident business benefit, available and suitable data plus a manageable technical connection. Together we prioritise the use cases where impact and feasibility stand in the best ratio.
How do we secure data protection and control over our data with integrated AI?
Data protection and control over your data we plan in from the start. We establish which data is processed, where it stays and who may reach what, and align the integration with your existing permission and security requirements. Where it makes sense, access control and traceability procedures are used. Concrete legal and regulatory requirements we examine individually in each context, in order to shape solutions that are solid and fitting.
How do we measure the benefit of an AI integration day to day?
We tie the benefit to business goals agreed in advance. Sensible measures are processing steps saved, faster retrieval of information, output quality that holds or improves plus the actual usage by the teams. Feedback from users adds important signals. We recommend starting with a tightly drawn use case, evidencing the impact there and then widening the integration where the value has been confirmed.
How does AI fit into existing system landscapes and permissions?
We connect AI capabilities through existing interfaces and place them in your architecture and operational processes. In doing so we respect established permission concepts, so users only see the information they are approved for anyway. Operation, updates and monitoring we plan together with your owners, so the integration stays maintainable. That way the solution grows with your requirements without changing the existing landscape needlessly.
CONTACT

Bring AI into your systems

We talk about entry points, models and embedding AI securely into your applications.

Thank you for your enquiry. We will get back to you personally shortly.
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